Determination of reference genes as a quantitative standard for gene expression analysis in mouse mesangial cells stimulated with TGF-β

Reverse transcription-quantitative polymerase chain reaction (RT-PCR) is the gold standard technique for gene expression analysis, but the choice of quantitative reference genes (housekeeping genes, HKG) remains challenging. Identify the best HKG is essential for estimating the expression level of target genes. Therefore, the aim of this study was to determine the best HKG for an in vitro model with mouse mesangial cells (MMCs) stimulated with 5 ng/mL of TGF-β. Five candidates HKG were selected: Actb, Hprt, Gapdh, 18S and Ppia. After quantitative expression, the best combination of these genes was analyzed in silico using six software programs. To validate the results, the best genes were used to normalize the expression levels of fibronectin, vimentin and α-SMA. In silico analysis revealed that Ppia, Gapdh and 18S were the most stable genes between the groups. GenEX software and Spearman's correlation determined Ppia and Gapdh as the best HKG pair, and validation of the HKG by normalizing fibronectin, vimentin and α-SMA were consistent with results from the literature. Our results established the combination of Ppia and Gapdh as the best HKG pair for gene expression analysis by RT-PCR in this in vitro model using MMCs treated with TGF-β.


Results
Candidate housekeeping genes. First, we followed a stepwise strategy to identify the best HKG expression by RT-PCR analysis. The workflow diagram is shown in Fig. 1. The samples were classified into three groups: (1) control cells (n = 6); (2) cells treated with TGF-β (n = 6); and (3) All, which includes all cells (n = 12). The Ct values of the five candidate HKG ranged between 23.511-9.387 and are expressed as [median (interquartile range)]. Ct values are inversely proportional to gene expression, and the mean highest Ct value among the candidate genes was achieved by Hprt [20.876 (2.05)], indicating the least expressed gene. In contrast, the lowest Ct value was obtained for 18S [10.232 (0.50)], indicating the highest expression among the candidate genes. Gapdh [17.948 (2.41)], followed by Actb [15.986 (2.44)] and Ppia [15.514 (1.54)], showed moderate expression. The median Ct values of triplicate reactions according to each gene is shown in Fig. 2A. Furthermore, no statistically significant differences were found between the control group and the TGF-β-treated group. Thus, these data suggest that the endogenous genes selected showed good stability and that their expression did not change when treated with TGF-β. Stability analysis of housekeeping genes. Stability analysis of the five candidate HKG were determined using six software packages (Supplementary Table 1). Genes with the smallest stability value (SV) had the most stable expression. Following NormFinder criteria (SD < 0.5), only Ppia and Gapdh had a SD value below 0.5 in the All and control groups, while in the TGF-β-treated group, only Ppia respects this criterion ( Fig. 2B and Supplementary Table 1). The GeNorm software defines an M value < 1.5; therefore, the genes with lower variability were: Hprt, 18S and Ppia for All samples; Gapdh, Ppia, Hprt and 18S for the control group; and 18S, Hprt and Ppia for the TGF-β-treated group (Fig. 2C and Supplementary Table 1). According to Bestkeeper criteria, when considering all three experimental groups, only 18S fits the parameters, although the CV exceeds 3.0 in the TGF-β group ( Fig. 2D and Supplementary Table 1).
In the RefFinder and ∆Ct method analysis, the most stable gene in the All group was Ppia, while in the control group it was Gapdh. However, in the TGF-β group, the most stable genes were 18S and Ppia according to Ref-Finder and ∆Ct method analysis, respectively (Fig. 2E-F and Supplementary Table 1). The evaluation of the most stable gene on DataAssist software identified that Ppia as the best HKG in the All, control, and TGF-β-treated groups (Fig. 2G and Supplementary Table 1). In all software analysis, the less stable gene was Actb (Fig. 2B-G  and Supplementary Table 1). Based on software analysis and a qualitative inspection of all ranks generated, the best HKG for the All group was Ppia, whereas it was Gapdh for the control group and Ppia or 18S for the TGFβ-treated group (Fig. 2B-G and Supplementary Table 1).
Analysis of the best combination of housekeeping genes. The GeNorm software recommends at least two genes for gene expression normalization, and the best combination of HKG for each group/software package is shown in Table 1. In the All group, the best pair of HKG is Ppia + Gapdh or Ppia + 18S; in the control group is Gapdh + Ppia and in the TGF-β group is Ppia + 18S (Table 1). To analyze the effects of the best candidate HKG, the expression levels of the top three (Ppia, Gapdh, and 18S) were normalized by each other (Fig. 3). All comparisons showed no statistically differential expression, meaning that Ppia, Gapdh, and 18S did not differ between the groups when normalized by each other (Fig. 3).  by their stability values, the optimal number of candidate genes to be used in each dataset must be established. The Acc.SD results showed that one gene (Ppia or 18S) is the optimal number of HKG for normalization of gene expression in TGF-β-treated samples (Fig. 4). Two genes are required for normalization in the All group (Ppia + Gapdh or Ppia + 18S) and in the control group (Gapdh + Ppia) (Fig. 4).
Correlation between the top three candidate housekeeping gene expressions. Correlation analysis were performed using the gene expression data from all samples. The expression levels of the three best candidate HKG showed a strong correlation between Ppia and Gapdh (ρ = 0.804, p = 0.002) (Fig. 5). Also, no statistically significant correlation was found between Ppia and 18S (ρ = −0.392, p = 0.208) and Gapdh and 18S (ρ = −0.580, p = 0.052) (Fig. 5). These results suggest that besides Ppia and Gapdh showed a strong correlation, they are correlated in all the samples and can be used together as suitable HKG.   (Fig. 6). The expression levels of fibronectin, vimentin, and α-SMA target genes were consistent with upregulation in the TGF-β group relative to controls (Fig. 6). The normalization of target genes by the two less stable genes (Actb and Hprt) was also evaluated ( Supplementary Fig. 1). The use of these HKG, whether alone or in combination, was not able to demonstrate the statistically significant difference that was expected between controls and TGF-β-treated samples of fibronectin and α-SMA ( Supplementary Fig. 1).

Discussion
The broadly accepted method used to normalize gene expression through RT-PCR technology involves the expression of endogenous HKG. However, the utility of HKG must be validated for specific experimental conditions, since the expression of these endogenous genes can vary depending on experimental conditions [8][9][10]15 .
In vitro systems, including cultured MCs, constitute an useful model to study many pathophysiological states affecting the glomeruli, such as glomerulosclerosis 1 . Therefore, we aimed to determine the most stable reference genes for mRNA quantification in studies performed in vitro, mimicking the in vivo glomerular fibrosis using MMCs treated with TGF-β 10,13,15,30 .
Since each algorithm ranked the best candidate HKG, the software packages recommended Ppia, Gapdh and 18S as the most stable reference genes between the groups. Peptidylprolyl isomerase A (Ppia), a highly abundant protein in the cytoplasm, takes part in various intracellular functions, including a homeostatic role in protein folding and trafficking, intracellular signaling, transcription, inflammation, apoptosis, and regulation of other proteins [31][32][33] . Glyceraldehyde-3-phosphate dehydrogenase (Gapdh) catalyzes the sixth reaction of anaerobic glycolysis, which produces ATP and pyruvate. Other than metabolic functions, this enzyme has been implicated in non-metabolic processes, such as apoptosis induction, DNA repair, cellular proliferation, and transcriptional activation [34][35][36] . Small subunit 18S ribosomal RNA (18S) is the smallest component of eukaryotic cytoplasmic ribosomes and is used as one of the molecular markers 37,38 .
The other two genes considered in this study (Hprt and Actb) are also commonly used as reference genes. Hypoxanthine phosphoribosyltransferase (Hprt) is responsible for purine metabolism, and deficiency of this gene dysregulates cell cycle-controlling functions and cell proliferation mechanisms 39,40 . Actin beta (Actb), which is highly abundant in eukaryotic cells, is essential for a variety of cellular functions and is involved in maintaining the cell's structure, integrity, and motility 41 . Although extensively used as reference genes 14 , Hprt and Actb ranked as the least stable in this study; however, further studies are needed to better delineate the interactions of these genes with TGF-β.
Since Ppia, Gapdh, and 18S were the most suitable candidate reference genes, we normalized them by each other, resulting in no statistically significant differences between groups, which suggests that these genes are good choices for our experimental conditions. After determining the candidate HKG by their stability values, we established the optimal number of reference genes using GenEx software. According to calculated Acc.SD, the optimal number of HKG in this model is the combination of two genes. When used together, Ppia and Gapdh showed a strong correlation, indicating that all samples were correlated and validating the best pair of HKG.
It is well demonstrated that TGF-β stimulates production of fibronectin, vimentin, and α-SMA in cultured MCs 2,42 ; thus, the best HKG combinations herein determined were used to normalize these target genes. Several studies have reported that Ppia 14,17,43-48 , Gapdh 44,49,50 , and 18S 45,51-53 are suitable reference genes and could be used as normalizers of target genes in different models. In the present study, the top three candidate reference genes, whether used alone or in combination, showed the expected increase in the expression of the target genes in the TGF-β-treated group. In contrast, the less stable HKG, employed alone or in combination, did not yield these expected differences, indicating that the in silico analysis selected the better, more stable HKG for this in vitro fibrosis model; they also revealed that an inadequate choice of the endogenous standard HKG could influence the results.
It is important to mention that other non-tested genes can also be used for normalization of the expression of target genes, and additional studies are needed to identify additional candidate genes. Furthermore, this study is specific to MMCs stimulated with TGF-β; thus, the conclusions drawn from our study are not transferable to other models that employ MMCs.
Validating gene expression stability of reference genes is crucial for reliable normalization of RT-PCR data. The work herein presented will serve as a reference for future studies using MMCs stimulated with TGF-β and allow a higher reliability and reproducibility in the identification of gene expression alterations.
Among the reference genes tested in this study, the combination of Ppia and Gapdh was the best HKG pair and should, therefore, be used as HKG in gene expression analysis in TGF-β-treated MMCs models.  The culture flasks were maintained in a 95% air atmosphere and 5% CO 2 humidified environment. At confluence, cells were exposed to DMEM/F12 medium containing no FBS for 24 h according to the following experimental groups: control, cultured in DMEM/F12 medium and TGF-β group, cultured in DMEM/F12 containing 5 ng/mL of recombinant TGF-β. After 24 h of incubation, cells were rinsed twice with PBS, and 1 mL of the commercial kit (TRIzol, Gibco BRL, Rockland, MD, USA) was added to isolate total RNA and evaluate the mRNA expression.  , following the authors' recommendations. These software packages determine the relative expression stability of the candidate HKG and generate a rank of the best genes 16 . NormFinder is a freely available tool and recommends a standard deviation less than 0.5 (SD < 0.5). GeNorm software calculates the gene stability measure (M value) and recommends that this value falls below 1.5 (M < 1.5). BestKeeper evaluates the SD and coefficient of variation (CV) of the samples, and a SD of less than 1.0 (SD < 1.0) and a CV of less than 3.0 (CV < 3.0) are required. DataAssist shows the Ct values of the candidate genes for all samples and organizes them by score. The comparative ΔCt method was used to calculate the mean SD of the samples. RefFinder software includes all the above software and calculates the geometric mean (Geomean). The optimal number of HKG was evaluated using the GenEx software package, which calculates the accumulated standard deviation (Acc.SD) of sample groups and estimates the ideal number of genes for normalization. The following groups were evaluated: control, TGF-β-treated and All (control + TGF-β samples).